In silico design of a multiepitope subunit vaccine targeting Salmonella enterica serovar Infantis: an immunoinformatics and reverse vaccinology approach
<i>Salmonella enterica</i> serovar Infantis is an emerging zoonotic pathogen increasingly linked to poultry and multidrug-resistant human infections. Existing vaccines lack serovar-specific efficacy, underscoring the need for targeted immunization strategies. In this study, we employed an immunoinformatics and reverse vaccinology pipeline to design a multiepitope subunit vaccine (MEV) against <i>S.</i> Infantis. A subtractive proteomics analysis of 692 poultry-derived genomes identified CsgA as a highly conserved, immunogenic, and non-host homologous antigen. Selected cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes were filtered for antigenicity, immunogenicity, allergenicity, and sequence conservation. The epitopes were assembled with appropriate linkers and a cholera toxin B (CTB) adjuvant. Structural modeling and normal mode analysis confirmed the construct's stability, while molecular docking predicted high-affinity interactions with chicken TLR2, TLR5, MHC-I, and MHC-II. Immune simulations indicated robust humoral and cellular responses. Codon optimization yielded a codon adaptation index (CAI) of 1.0 and GC content of 54.76%, supporting efficient expression in <i>E. coli.</i> The optimized construct was successfully cloned in silico into the pET30a(+) vector. These findings present a rationally designed, computationally validated vaccine candidate with the potential to induce protective immunity against <i>S.</i> Infantis, warranting further experimental validation.
Authors
- Dhiraj Chundru (ORCID: https://orcid.org/0009-0008-3085-381X)
- Mostafa Ghanem (ORCID: https://orcid.org/0000-0001-9647-1357)
Institutions
- Virginia–Maryland College of Veterinary Medicine (US)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2026-02-06
- DOI
- https://doi.org/10.3389/fimmu.2026.1717278
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.00